Understanding AI: A Practical Guide

Artificial intelligence is transforming how organisations operate, how decisions get made, and how people work. But despite the hype (and sometimes because of it) there's widespread confusion about what these systems actually are, how they work, and what they can and can't do.

This guide cuts through the noise. It's designed to give you a clear, grounded understanding of AI systems without requiring a technical background. You won't find breathless predictions about superintelligence or dismissive claims that it's all just hype. Instead, you'll find straightforward explanations of the technology, its capabilities, its limitations, and why trustworthiness matters.

What This Guide Covers

Glossary: A quick reference guide for some of the terms used throughout these pages, with links to some great foundational resources on these topics.

How AI Chatbots Work: What actually happens when you type a message and get a response. Training, tokens, embeddings, attention, prediction, explained with limited jargon. Plus: how we got to our current capabilities (spoiler: scale, not breakthroughs).

How Other AI Systems Work: Beyond chatbots: image generation, video generation, recommendation systems, predictive models, computer vision, and speech systems. The common thread across all of them.

How AI Agents Work: What happens when AI outputs connect to real systems that take actions. The infrastructure that makes agents possible, the risks they introduce, and why removing the human from the loop raises the stakes.

Why Trustworthy AI Matters: AI systems are shaped by human decisions at every stage. Adoption is stalling because of a trust gap. We look at what makes an AI system trustworthy, and why you'd never accept less from a human colleague.

The Cost of Compute: AI doesn't run on magic. It runs on electricity and specialised hardware, and needs water, and lots of all three. Understanding these costs helps explain why AI systems work the way they do, why some capabilities are more expensive than others, and why the infrastructure behind AI is becoming a significant economic and environmental issue.

The Core Message

Throughout this guide, you'll encounter a consistent theme: AI systems are tools built by humans, trained on human-generated data, and deployed according to human decisions. They encode statistical patterns, not understanding. They generate probable outputs, not truth.

This isn't a limitation to apologise for, it's simply what the technology is. Understanding this clearly is the foundation for using AI effectively, governing it appropriately, and building systems that deserve trust.